{
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    "# Visual Diagnosis of Text Analysis with Baleen \n",
    "\n",
    "This notebook has been created as part of the [Yellowbrick user study](http://www.scikit-yb.org/en/latest/evaluation.html). I hope to explore how visual methods might improve the workflow of text classification on a small to medium sized corpus. \n",
    "\n",
    "## Dataset \n",
    "\n",
    "The dataset used in this study is a sample of the [Baleen Corpus](http://baleen.districtdatalabs.com/). The Baleen corpus has been ingesting RSS feeds on the hour from a variety of topical feeds since March 2016, including news, hobbies, and political documents and currently has over 1.2M posts from 373 feeds. [Baleen](https://github.com/bbengfort/baleen) (an open source system) has a sister library called [Minke](https://github.com/bbengfort/minke) that provides multiprocessing support for dealing with Gigabytes worth of text. \n",
    "\n",
    "The dataset I'll use in this study is a sample of the larger data set that contains 68,052 or roughly 6% of the total corpus. For this test, I've chosen to use the preprocessed corpus, which means I won't have to do any tokenization, but can still apply normalization techniques. The corpus is described as follows:\n",
    "\n",
    "Baleen corpus contains 68,052 files in 12 categories.\n",
    "Structured as:\n",
    "\n",
    "- 1,200,378 paragraphs (17.639 mean paragraphs per file)\n",
    "- 2,058,635 sentences (1.715 mean sentences per paragraph).\n",
    "\n",
    "Word count of 44,821,870 with a vocabulary of 303,034 (147.910 lexical diversity).\n",
    "\n",
    "Category Counts: \n",
    "\n",
    "- books: 1,700 docs\n",
    "- business: 9,248 docs\n",
    "- cinema: 2,072 docs\n",
    "- cooking: 733 docs\n",
    "- data science: 692 docs\n",
    "- design: 1,259 docs\n",
    "- do it yourself: 2,620 docs\n",
    "- gaming: 2,884 docs\n",
    "- news: 33,253 docs\n",
    "- politics: 3,793 docs\n",
    "- sports: 4,710 docs\n",
    "- tech: 5,088 docs\n",
    "\n",
    "This is quite a lot of data, so for now we'll simply create a classifier for the \"hobbies\" categories: e.g. books, cinema, cooking, diy, gaming, and sports. \n",
    "\n",
    "Note: this data set is not currently publically available, but I am happy to provide it on request. "
   ]
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   "source": [
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "import os \n",
    "import sys \n",
    "import nltk\n",
    "import pickle\n",
    "\n",
    "# To import yellowbrick \n",
    "sys.path.append(\"../..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Loading Data \n",
    "\n",
    "In order to load data, I'd typically use a `CorpusReader`. However, for the sake of simplicity, I'll load data using some simple Python generator functions. I need to create two primary methods, the first loads the documents using pickle, and the second returns the vector of targets for supervised learning. "
   ]
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   "source": [
    "CORPUS_ROOT = os.path.join(os.getcwd(), \"data\") \n",
    "CATEGORIES = [\"books\", \"cinema\", \"cooking\", \"diy\", \"gaming\", \"sports\"]\n",
    "\n",
    "def fileids(root=CORPUS_ROOT, categories=CATEGORIES): \n",
    "    \"\"\"\n",
    "    Fetch the paths, filtering on categories (pass None for all). \n",
    "    \"\"\"\n",
    "    for name in os.listdir(root):\n",
    "        dpath = os.path.join(root, name)\n",
    "        if not os.path.isdir(dpath):\n",
    "            continue \n",
    "        \n",
    "        if categories and name in categories: \n",
    "            for fname in os.listdir(dpath):\n",
    "                yield os.path.join(dpath, fname)\n",
    "\n",
    "\n",
    "def documents(root=CORPUS_ROOT, categories=CATEGORIES):\n",
    "    \"\"\"\n",
    "    Load the pickled documents and yield one at a time. \n",
    "    \"\"\"\n",
    "    for path in fileids(root, categories):\n",
    "        with open(path, 'rb') as f:\n",
    "            yield pickle.load(f)\n",
    "\n",
    "\n",
    "def labels(root=CORPUS_ROOT, categories=CATEGORIES):\n",
    "    \"\"\"\n",
    "    Return a list of the labels associated with each document. \n",
    "    \"\"\"            \n",
    "    for path in fileids(root, categories):\n",
    "        dpath = os.path.dirname(path) \n",
    "        yield dpath.split(os.path.sep)[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": true
   },
   "source": [
    "### Feature Extraction and Normalization \n",
    "\n",
    "In order to conduct analyses with Scikit-Learn, I'll need some helper transformers to modify the loaded data into a form that can be used by the `sklearn.feature_extraction` text transformers. I'll be mostly using the `CountVectorizer` and `TfidfVectorizer`, so these normalizer transformers and identity functions help a lot. "
   ]
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   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "from nltk.corpus import wordnet as wn\n",
    "from nltk.stem import WordNetLemmatizer \n",
    "from unicodedata import category as ucat\n",
    "from nltk.corpus import stopwords as swcorpus\n",
    "from sklearn.base import BaseEstimator, TransformerMixin \n",
    "\n",
    "\n",
    "def identity(args):\n",
    "    \"\"\"\n",
    "    The identity function is used as the \"tokenizer\" for \n",
    "    pre-tokenized text. It just passes back it's arguments. \n",
    "    \"\"\"\n",
    "    return args \n",
    "\n",
    "\n",
    "def is_punctuation(token):\n",
    "    \"\"\"\n",
    "    Returns true if all characters in the token are\n",
    "    unicode punctuation (works for most punct). \n",
    "    \"\"\"\n",
    "    return all(\n",
    "        ucat(c).startswith('P')\n",
    "        for c in token \n",
    "    )\n",
    "\n",
    "\n",
    "def wnpos(tag):\n",
    "    \"\"\"\n",
    "    Returns the wn part of speech tag from the penn treebank tag. \n",
    "    \"\"\"\n",
    "    return {\n",
    "        \"N\": wn.NOUN,\n",
    "        \"V\": wn.VERB,\n",
    "        \"J\": wn.ADJ, \n",
    "        \"R\": wn.ADV, \n",
    "    }.get(tag[0], wn.NOUN)\n",
    "\n",
    "\n",
    "class TextNormalizer(BaseEstimator, TransformerMixin):\n",
    "    \n",
    "    def __init__(self, stopwords='english', lowercase=True, lemmatize=True, depunct=True):\n",
    "        self.stopwords  = frozenset(swcorpus.words(stopwords)) if stopwords else frozenset()\n",
    "        self.lowercase  = lowercase \n",
    "        self.depunct    = depunct \n",
    "        self.lemmatizer = WordNetLemmatizer() if lemmatize else None \n",
    "    \n",
    "    def fit(self, docs, labels=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, docs): \n",
    "        for doc in docs: \n",
    "            yield list(self.normalize(doc)) \n",
    "    \n",
    "    def normalize(self, doc):\n",
    "        for paragraph in doc:\n",
    "            for sentence in paragraph:\n",
    "                for token, tag in sentence: \n",
    "                    if token.lower() in self.stopwords:\n",
    "                        continue \n",
    "                    \n",
    "                    if self.depunct and is_punctuation(token):\n",
    "                        continue \n",
    "                    \n",
    "                    if self.lowercase:\n",
    "                        token = token.lower() \n",
    "                    \n",
    "                    if self.lemmatizer:\n",
    "                        token = self.lemmatizer.lemmatize(token, wnpos(tag))\n",
    "                    \n",
    "                    yield token "
   ]
  },
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    "editable": true
   },
   "source": [
    "### Corpus Analysis \n",
    "\n",
    "At this stage, I'd like to get a feel for what was in my corpus, so that I can start thinking about how to best vectorize the text and do different types of counting. With the Yellowbrick 0.3.3 release, support has been added for two text visualizers, which I think I will test out at scale using this corpus. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'transform'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-3514380b0c82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m ])\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'viz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlast_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    304\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlast_step\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    495\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transform'"
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fiBEjkiRvvfXWoNUBAACUqur3watUKknS7cVXTqipqRm0OgAAgFJV/b4Bo0ePTpK0trZ2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\noVAoFAqFQqG8I/wf27lDuEaJvt4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ca22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "from yellowbrick.text import FreqDistVisualizer\n",
    "\n",
    "visualizer = Pipeline([\n",
    "    ('norm', TextNormalizer()),\n",
    "    ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n",
    "    ('viz', FreqDistVisualizer())\n",
    "])\n",
    "\n",
    "visualizer.fit_transform(documents(), labels())\n",
    "visualizer.named_steps['viz'].poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Aq6OsV/mU/hcMCZMAcF6pB7zatWtLko4fP+62Lz09XVWq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x9/4NKlSzhy5Ah+++037N+/H61atcKYMWNga2vLqWtZWlpyOzHJyclwcHCQOz8+NzcXixcvliu4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\nIIiPBArwCIIgCIIgCIIgPhL+D4YIpwprbr3zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11752cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vect = Pipeline([\n",
    "    ('norm', TextNormalizer()),\n",
    "    ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n",
    "])\n",
    "\n",
    "docs = vect.fit_transform(documents(), labels())\n",
    "viz = FreqDistVisualizer() \n",
    "viz.fit(docs, vect.named_steps['count'].get_feature_names())\n",
    "viz.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b5e0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer \n",
    "from yellowbrick.text import TSNEVisualizer\n",
    "\n",
    "vect = Pipeline([\n",
    "    ('norm', TextNormalizer()),\n",
    "    ('tfidf', TfidfVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n",
    "])\n",
    "\n",
    "docs = vect.fit_transform(documents(), labels())\n",
    "\n",
    "viz = TSNEVisualizer() \n",
    "viz.fit(docs, labels())\n",
    "viz.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification \n",
    "\n",
    "The primary task for this kind of corpus is classification - sentiment analysis, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts \n",
    "\n",
    "docs_train, docs_test, labels_train, labels_test = tts(docs, list(labels()), test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from yellowbrick.classifier import ClassBalance, ClassificationReport, ROCAUC\n",
    "\n",
    "logit = LogisticRegression()\n",
    "logit.fit(docs_train, labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C+SvhwSTAnwcjbgCKVGRkpL799lv16dNHlSpVUmJiouLj43X27Fm1aNFCkydP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\nvy9JCgkJ0SeffKLGjRtLksq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GjNGWLVv0wAMPqGnTpurVq5d27Nih7OxsPfvs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146a61940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_balance = ClassBalance(logit, classes=set(labels_test))\n",
    "logit_balance.score(docs_test, labels_test)\n",
    "logit_balance.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-7b911cc79c37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ee61da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_balance = ClassificationReport(logit, classes=set(labels_test))\n",
    "logit_balance.score(docs_test, labels_test)\n",
    "logit_balance.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-3e54aae07a7d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13466ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_balance = ClassificationReport(LogisticRegression())\n",
    "logit_balance.fit(docs_train, labels_train)\n",
    "logit_balance.score(docs_test, labels_test)\n",
    "logit_balance.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data is not binary and pos_label is not specified",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-5eccb2a02e03>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m         \"\"\"\n\u001b[1;32m    312\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthresholds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m    503\u001b[0m     \"\"\"\n\u001b[1;32m    504\u001b[0m     fps, tps, thresholds = _binary_clf_curve(\n\u001b[0;32m--> 505\u001b[0;31m         y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m     \u001b[0;31m# Attempt to drop thresholds corresponding to points in between and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m    312\u001b[0m              \u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m              array_equal(classes, [1]))):\n\u001b[0;32m--> 314\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data is not binary and pos_label is not specified\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    315\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0mpos_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Data is not binary and pos_label is not specified"
     ]
    }
   ],
   "source": [
    "logit_balance = ROCAUC(logit)\n",
    "logit_balance.score(docs_test, labels_test)\n",
    "logit_balance.poof()"
   ]
  }
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